Background & Context

Employee Promotion means the ascension of an employee to higher ranks, this aspect of the job is what drives employees the most. The ultimate reward for dedication and loyalty towards an organization and HR team plays an important role in handling all these promotion tasks based on ratings and other attributes available.

The HR team in JMD company stored data of promotion cycle last year, which consists of details of all the employees in the company working last year and also if they got promoted or not, but every time this process gets delayed due to so many details available for each employee - it gets difficult to compare and decide.

So this time HR team wants to utilize the stored data to make a model, that will predict if a person is eligible for promotion or not.

You as a data scientist at JMD company, need to come up with a model that will help the HR team to predict if a person is eligible for promotion or not.

Objective

Explore and visualize the dataset. Build a classification model to predict if the customer has a higher probability of getting a promotion. Optimize the model using appropriate techniques. Generate a set of insights and recommendations that will help the company.

Data Dictionary

Submission Guidelines

There are two parts to the submission:

  1. A well commented Jupyter notebook [format - .ipynb]
  2. A submission in html format Any assignment found copied/ plagiarized with other groups will not be graded and awarded zero marks Please ensure timely submission as any submission post-deadline will not be accepted for evaluation

Submission will not be evaluated if:

  1. it is submitted post-deadline, or,
  2. more than 2 files are submitted

Happy Learning!!

Scoring Guide

Perform an Exploratory Data Analysis on the data. Points: 6

Illustrate the insights based on EDA. Points: 5

Data Pre-processing. Points: 6

Model building - Logistic Regression. Points: 6

Model building - Bagging and Boosting. Points: 9

Hyperparameter tuning using grid search. Points: 9

Hyperparameter tuning using random search. Points: 9

Model Performances. Points: 5

Actionable Insights & Recommendations. Points: 5

Total Points: 60

Import Packages and Dataset

Insights

EDA

Insights

Insights

Address Null Values

Insights

Transform Categorical Columns

Transform DTypes

EDA, cont.

Pre-Graph Insights

Post-Graph Insights

Quantile Insights

Bi-variate Analysis

Insights

Insights

Insights

Insights

Feature Engineering

Insights

Insights

Insights

Models

Linear Regression

Decision Tree

Random Forest

AdaBoost

Gradient Boosting

Hyper-parameter Tuning